CodeLabeller: A Web-based Code Annotation Tool for Java Design Patterns and Summaries

While constructing supervised learning models, we require a labelled example to build a corpus and train a machine learning model. However, majority of studies have built the labelled dataset manually, which in many occasions is a daunting task. To mitigate this problem, we have built an online tool called CodeLabeller. CodeLabeller is a web-based tool that aims to provide an efficient approach in handling the process of labelling Java source files for supervised learning methods at scale by improving the data collection process throughout. CodeLabeller is tested by constructing a corpus of over a thousand source files obtained from a large collection of open-source Java projects and labelling each Java source file with their respective design patterns and summaries. 10 experts in the field of software engineering participated in a usability evaluation of the tool on UEQ-S. The survey demonstrates that the tool is easy to use and meet the needs of the labelling the corpus for supervised classifiers. Apart from assisting researchers to crowdsource a labelled dataset, the tool has practical applicability in software engineering education and assists in building expert ratings for software artefacts.